Hybrid of Supervised Learning and Optimization Algorithm for Optimal Detection of IoT Distributed Denial of Service Attacks

Authors

  • Talha Farid Department of Computing, Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Johor, Malaysia
  • Maheyzah Sirat Department of Computing, Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/ijic.v13n1.329

Keywords:

Internet of Things (IoT) cybersecurity, Distributed Denial of Service (DDoS) Attack, Supervised Machine Learning

Abstract

The high-speed internet has led to the development of Internet of Things (IoT) with a fundamental Three-Layer IoT architecture. However, small amount of un-indicative data captured at the end level of IoT network makes the edge IoT devices susceptible to cyber-security attacks aimed at its transport layer. The Distributed Denial of Service (DDoS) poses significant cyber-security threat to the heterogenous IoT devices which are rendered vulnerable by ineffectiveness of conventional cybersecurity softwares. The literature reveals numerous studies that employed machine learning for the mitigation of IoT DDoS attacks but they lack in terms of an extensive investigation on optimization of machine learning classifiers. Therefore, this study first evaluates the prediction performance of machine learning classification algorithms trained on an authenticated/validated real-time IoT traffic dataset. The results reveal Logistic Regression (LR) as the most effective supervised machine learning classifier for detecting IoT DDoS attacks with a prediction accuracy of 97%. Following this, another investigation on the hybridization of LR with optimization algorithms yields Grasshopper Optimizer Algorithms (GOA) as the most effective optimizer in improving its prediction accuracy to 99%. Hence, the LR hybridized by GOA is developed as the optimal IoT DDoS Attack detection solution. Thus, the study serves to lay the foundation of a data-driven approach for the mitigation of the emerging variants of malicious IoT DDoS attacks such as zero-day attacks.

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Published

2023-05-30

How to Cite

Farid, T., & Sirat, M. (2023). Hybrid of Supervised Learning and Optimization Algorithm for Optimal Detection of IoT Distributed Denial of Service Attacks. International Journal of Innovative Computing, 13(1), 1–12. https://doi.org/10.11113/ijic.v13n1.329

Issue

Section

Computer Science